User equipments (UE), also known as mobile stations, wireless terminals and/or mobile terminals are enabled to communicate wirelessly in a wireless communication system, sometimes also referred to as a cellular radio system. The communication may be made e.g. between two user equipment units, between a user equipment and a regular telephone and/or between a user equipment and a server via a Radio Access Network (RAN) and possibly one or more core networks.
The user equipment units may further be referred to as mobile telephones, cellular telephones, laptops with wireless capability. The user equipment units in the present context may be, for example, portable, pocket-storable, hand-held, computer-comprised, or vehicle-mounted mobile devices, enabled to communicate voice and/or data, via the radio access network, with another entity, such as another user equipment or a server.
The wireless communication system covers a geographical area which is divided into cell areas, with each cell area being served by a network node, or base station e.g. a Radio Base Station (RBS), which in some networks may be referred to as “eNB”, “eNodeB”, “NodeB” or “B node”, depending on the technology and terminology used. The network nodes may be of different classes such as e.g. macro eNodeBs, home eNodeBs or pico base stations, based on transmission power and thereby also cell size. A cell is the geographical area where radio coverage is provided by the network node/base station at a base station site and may also be referred to as a radio coverage area. One base station, situated on the base station site, may serve one or several cells by having one or several radio transmitters. The network nodes communicate over the air interface operating on radio frequencies with the user equipment units within range of the respective network node.
In some radio access networks, several network nodes may be connected, e.g. by landlines or microwave, to a Radio Network Controller (RNC) e.g. in Universal Mobile Telecommunications System (UMTS). The RNC, also sometimes termed a Base Station Controller (BSC) e.g. in GSM, may supervise and coordinate various activities of the plural network nodes connected thereto. GSM is an abbreviation for Global System for Mobile Communications (originally: Groupe Spécial Mobile).
UMTS is a third generation mobile communication system, which evolved from GSM, and is intended to provide improved mobile communication services based on Wideband Code Division Multiple Access (WCDMA) access technology. UMTS Terrestrial Radio Access Network (UTRAN) is essentially a radio access network using wideband code division multiple access for user equipment units.
The 3rd Generation Partnership Project (3GPP) has undertaken to evolve further the UTRAN and GSM based radio access network technologies, for example by developing the Long Term Evolution (LTE) and the Evolved Universal Terrestrial Radio Access Network (E-UTRAN).
High Speed Packet Access (HSPA) is a term encompassing two mobile telephony protocols, High Speed Downlink Packet Access (HSDPA) and High Speed Uplink Packet Access (HSUPA). HSPA extends and improves the performance of existing WCDMA protocols. It may here be mentioned that HSUPA also is known as Enhanced Uplink (EUL), in some literature, e.g. by 3GPP.
In the present context, the expressions downlink, downstream link or forward link may be used for the transmission path from the network node to the user equipment. The expression uplink, upstream link or reverse link may be used for the transmission path in the opposite direction i.e. from the user equipment to the network node.
In the HSPA uplink, the user equipments share the same time and frequency resource. When the Node B detects a signal from a specific user equipment, with a traditional RAKE receiver, the received power of the other user equipment and part of the received power of this specific user equipment, is regarded as interference to the specific user equipment at the Node B. In other words, the total received power at the Node B may be viewed as the cell load. When the total received power is high, the cell load is high. In practice, the uplink cell load is estimated in terms of the Rise Over Thermal (RoT) of the cell, which is the total received power divided by the thermal noise floor power. This may sometimes also be referred to as the noise rise, see FIG. 1A.
FIG. 1A illustrates the air interface load according to prior art. The pole capacity is the limiting theoretical bit rate of the uplink, corresponding to an infinite noise rise.
The noise rise may be seen as the total received power relative to the noise power within a cell. The noise rise is increasing with the number of user equipments and/or the radio traffic intensity within the cell. A general definition of noise rise in the linear domain is
                    η        =                                            I              tot                        N                    .                                    [        1        ]            
The total received power, Itot, in a cell comprises uplink power from all user equipments in the own cell, Iown, uplink WCDMA radio link power from user equipments in the neighbour cells, Inei, as well as the thermal noise floor power N thusItot=Iown+Inei+N  [2]
Considering a user equipment at the cell border attempting to connect to the cell, the total received power from all user equipments at the Node B, i.e. within the cell served by the Node B, is interference to this user equipment. If the interference level is too high, the limited power of the user equipment may not be able to ensure a successful connection to the Node B. This results in a coverage problem. Therefore, a first main aim of load control of HSUPA is to control the total received power at the Node B to be below a target so that a user equipment at the cell border may connect to the cell when it wants. The target may depend on the cell size: a lower target for a larger cell size, vice versa.
Besides the rise over thermal target considering the coverage limit there is another limit which may be considered when setting the noise rise target, namely the stability limit. The reason is that if the load in the cell is too high the interference between user equipments will cause power rushes and in-stability of the system. To address the stability issue, current ideas include subtraction of estimates of the neighbour cell interference from the total power. This may not be perfectly true, however normally the inter-cell interference coupling between user equipments is a lot weaker than the intra-cell interference coupling between user equipments.
Therefore, the overall cell load must not exceed either the coverage or the stability limitations.
In order to control the total load in the cell to be below the rise over thermal target, and the stability noise rise target, a load estimator needs to estimate the load generated by each radio connection and the available scheduling headroom that Enhanced Dedicated Channel (E-DCH) traffic may use.
The load factor of one radio connection is defined asLi=Pi/Itot,  [3]where Pi is the received signal power from user channel i. The load factor estimation is the basis for load control. Therefore, for example the Dedicated Physical Control CHannel (DPCCH) load of the user equipment i isLci=Pci/Itot,where Pci is the received DPCCH power of the user equipment i. The scheduling headroom, or the maximum allowed Enhanced Dedicated Channel (E-DCH) load in the cell is thusLmax EDCH=Lmax RoT−Lothers−LnonEDCH=1−1/RoTtarget−Lothers−LnonEDCH,  [4]where Lothers is the summed load of the inter-cell interference and LnonEDCH is the summed load of the non E-DCH channels, for example DCH and High Speed (HS)-DPCCH.
                              L          nonEDCH                =                              ∑                          #              ⁢              nonEDCH                                ⁢                                    P              nonEDCH                        /                                          I                tot                            .                                                          [        5        ]            
During scheduling, the scheduler may estimate the total E-DCH load with the allocated grant by summing the load factor of the E-DCH channels.
                              L          EDCH                =                              ∑                          #              ⁢              EDCH                                ⁢                                    P              EDCH                        /                                          I                tot                            .                                                          [        6        ]            
The scheduler may also estimate the Enhanced Transport Format Combinations (E-TFC) grant that may eat up the available load room based on the E-DCH Dedicated Physical Data Channel (E-DPDCH) load factor. The E-DPDCH load factor of user equipment i with E-TFC j isLE-TFCj,i=PE-TFCj,i/Itot=Pci·βj/Itot where βj is the E-DPDCH to DPCCH power offset and is one by one mapped to the corresponding E-TFCj.
Therefore, suppose the available load room for user equipment i is Lavail, the suitable E-TFC (or corresponding power offset) may be calculated asβj=Lavail/(Pci/Itot),  [7]which may be referred to as load to E-TFC mapping.
The estimation of rise over thermal relies on the measurement of the total interference and knowledge of the thermal noise power floor.
To understand the desire to apply sophisticated estimation techniques to find the thermal noise power floor, it is to be noted that the signal reference point is at the antenna connector, by definition. The interference measurements are however obtained after the analogue signal conditioning chain, in the digital receiver. The analogue signal conditioning chain does introduce a scale factor error of about 1 dB (1-sigma) that is difficult to compensate for. Fortunately, all contributing interference powers are equally affected by the scale factor error so when the rise over thermal is calculated, the scale factor error may be cancelled as
                                                                        RoT                Digitalreceiver                            =                            ⁢                                                I                                      tor                    ,                    air                                                                    N                  Digitalreceiver                                                                                                        =                            ⁢                                                scaleFactor                  ×                                      I                                          tot                      ,                      antenna                                                                                        scaleFactor                  ×                                      N                    Antenna                                                                                                                          =                            ⁢                                                RoT                  Antenna                                .                                                                        [        8        ]            
In order to understand the fundamental problem of neighbour cell interference when performing load estimation, note thatIneighbor+N=E└INeighbor(t)┘+E[N]+ΔINeighbor+ΔN,  [9]where E[ ] denotes mathematical expectation and where Δ denotes the variation around the mean. The fundamental problem may now be clearly seen. Since there are no measurements available in the network node that are related to the neighbour cell interference, a linear filtering operation may at best estimate the sum E└INeighbor┘+E[N]. This estimate cannot be used to deduce the value of E[N]. The situation is the same as when the sum of two numbers is available. Then there is no way to figure out the values of the individual numbers. This issue is analyzed rigorously for the rise over thermal estimation problem in equation [2] where it is proved that the noise power floor is not mathematically observable.
The rise over thermal estimation algorithm is depicted in FIG. 1B. It is described in detail in [3]. The main problem solved by the estimation algorithm may be the accurate estimation of the thermal noise floor N. Since it is not possible to obtain exact estimates of this quantity due to the neighbour cell interference, the estimator therefore applies an approximation, by consideration of the soft minimum as computed over a relative long window in time.
It is important to understand that this estimation relies on the fact that the noise floor is constant over very long periods of time, disregarding the small temperature drift.
The sliding window algorithm of the above section has the disadvantage of requiring a large amount of storage memory. This becomes particularly troublesome in case a large number of instances of the algorithm are needed, as may be the case when Interference Cancellation (IC) is introduced in the uplink.
Interference cancellation approaches for HSUPA, e.g., [5][6], are attractive for the purposes of achieving higher and higher uplink data rates. Typically, some form of iterative processing is employed in a multi-stage architecture. In each successive stage, interference is cancelled leading to improved detection of the desired signal(s). One such architecture for HSUPA is illustrated in FIG. 1C. In this exemplary architecture, the data channel for users 1 and 2 (E-DPDCH) is detected in a multi-stage fashion. These user equipments are referred to as the Multi-User Detection (MUD) user equipments.
The control channels for both the MUD user equipments and non-MUD user equipments (DPCCH, E-DPCCH, HS-DPCCH) are cancelled in various stages depending on when the control information is needed. For example, in order to detect the data channel, the E-DPCCH needs to be decoded, thus it is detected and cancelled in the first stage. The HS-DPCCH is detected, decoded, and cancelled in the second stage, since the information it carries is not critical to detecting the data channel. Delaying this channel to the second stage allows it to benefit from cancellation in the second stage. Similarly, the Physical Random Access Channel (PRACH) is detected in a later stage (e.g., stage-2, FIG. 1C) to allow it to benefit from interference cancellation (more on this below).
Note that only after the first stage will any signal benefit from the cancellation of other signals since the signals in the first stage are detected in parallel in the presence of the maximum level of interference. The block diagram shows the interference cancellation taking place in an “Antenna Buffer” indicated by the “Ant Buf” blocks. As such, the antenna buffer after each stage contains a “cleaned up” version of the original received signal. It is cleaned up in the sense that interference is cancelled.
As mentioned above, the PRACH is not detected until a later stage (e.g., stage-2 in FIG. 1C) to allow it to benefit from interference cancellation in earlier stages. Ideally, one would like to delay the detection of this signal as much as possible to maximize the benefit from interference cancellation; however, there is a limit due to the timing constraints on this channel set by the standard. There is a delay budget between the time the Random Access Channel (RACH) preamble is signalled by the user equipment and the time the user equipment received back an acquisition indicator (AICH) message indicating successful RACH detection. For this reason, the RACH detection is placed in stage-2.
The Random Access Channel is a shared channel that is used by user equipments in the cell e.g. for initial access to the network, without being scheduled.
FIG. 1C which may be a suitable trade-off between the interference cancellation benefit and the timing constraints. If the interference cancellation processing is very fast, then the RACH detection could be delayed further, thus increasing the benefit from interference cancellation without violating the timing constraints.
With interference cancellation between user equipments, to reach the Signal to Interference and Noise Ratio (SINR) target for a certain transport format, the required transmit power of a user may be reduced driven by the power control function. As a consequence, the load in the cell may be reduced, and more scheduling headroom may be left for the E-DCH traffic. Therefore, the cell throughput is expected to increase with interference cancellation.
Furthermore, further throughput gain could be expected from increasing the allowed cell load considering that the interference as experienced by the user equipment is no longer the total received air interface power at the Node B. The stability may be much better controlled with interference cancellation so that the load limit considering stability could be further released. Therefore, the cell load may be controlled based on the user experienced load after interference cancellation processing.
FIG. 1D illustrates the general principles of an interference cancellation process. When the transmission of a user equipment has been detected, or even decoded, the so obtained signal may be used to re-generate a model signal, a replica signal, that resembles the effect of the original transmitted signal of the user equipment e.g. at the antenna or at another point in the receiver chain. The creation of the signal always requires that the channel model is available, to capture the effect of the radio transmission from the user equipment to the base station. The model signal may then be subtracted from the received broadband signal. In case the model signal is accurate, then the effect of the user equipment on the uplink may be reduced. Since this signal is only interference for the other user equipment, the effect of the subtraction is that interference as seen by the user equipment is cancelled, hence the acronym interference cancellation.
Interference cancellation exists in several variants. As indicated above interference cancellation may be based on demodulated IQ samples. Another option is to base the cancellation on decoded symbols. The latter option has the advantage of a better performance since the coding gain is exploited to enhance the quality of the model signal. On the other hand the delay is increased, due to the time needed for the decoding step. An additional delay is a consequence of most practical interference cancellation variants, since a model signal may need to be created before cancellation may be performed.
Another distinction of interference cancellation algorithms may be between soft and hard algorithms.
Today, much of the discussion on how to implement interference cancellation is focused on the different architectures, described in the following sub-sections. This is because interference cancellation may in general be computationally complex and also cause a time delay, which may be somewhat different for different interference cancellation architectures, as e.g. the ones illustrated in FIG. 1E and FIG. 1F.
FIG. 1E illustrates a structure of Successive Interference Cancellation (SIC). The block diagram is shown for detected signals. As may be seen in FIG. 1E, the detected signal of the first user equipment is immediately used to improve the conditions for all other user equipments, then the detected signal of the second user equipment is used to improve the conditions for all other user equipments but the first user equipment, and so on. This means that interference cancellation gains are achieved already at stage 1, however the delay of each stage will be dependent on the detection time of each user equipment. The delay may hence depend on the number of (interference cancellation) user equipments.
FIG. 1F illustrates the principles of Parallel Interference Cancellation (PIC). In parallel interference cancellation interference subtraction is only performed between stages. The consequence is that the number of units for regeneration and subtraction become large. The delay is however fix, a fact that simplifies system design.
The previously known solutions further increase the cell load mainly based on the fact that with interference cancellation the stability between user equipments is improved. For a multi-stage interference cancellation process, the interference cancellation gain after the final interference cancellation stage is captured. This is proper with for example the clean-carrier case, which means no new user equipment will connect to this carrier. However, for a normal case, the coverage limit for load control needs to be handled as well.
For a multi-stage interference cancellation process, the channel that limits the cell coverage (e.g. PRACH) may not be able to get the full interference cancellation gain at the final interference cancellation stage. For example, considering the delay that may be tolerated, the PRACH may have to be detected at some intermediate interference cancellation stage rather than at the final stage, and if the load is controlled based on the final interference cancellation stage, there will be a coverage problem. However, there are no algorithms or concepts known in prior art that are able to address the load experience after said some intermediate interference cancellation stage rather than the final interference cancellation stage. Moreover, prior art always controls the load based on interference cancellation gain estimated at a fixed interference cancellation stage (e.g. the final stage), and this may either overestimate or underestimate the available coverage budget.
Furthermore, known solutions estimate interference cancellation gains only based on the total wideband received power before and after interference cancellation, which is inaccurate especially in a multi-cell cases, where there exists inter-cell interference. Moreover, different channels of a user equipment may experience different interference cancellation gain (e.g. the control channel may have less interference cancellation gain due to different handling scheme), this is currently not considered.